knn.py

import numpy as np  
import matplotlib.pyplot as plt  
import pandas as pd

url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"

# Assign colum names to the dataset
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']

# Read dataset to pandas dataframe
dataset = pd.read_csv(url, names=names) 

X = dataset.iloc[:, :-1].values  
y = dataset.iloc[:, 4].values  

from sklearn.model_selection import train_test_split  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)  

from sklearn.preprocessing import StandardScaler  
scaler = StandardScaler()  
scaler.fit(X_train)

X_train = scaler.transform(X_train)  
X_test = scaler.transform(X_test) 

from sklearn.neighbors import KNeighborsClassifier  
classifier = KNeighborsClassifier(n_neighbors=5)  
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)  

from sklearn.metrics import classification_report, confusion_matrix  
print(confusion_matrix(y_test, y_pred))  
print(classification_report(y_test, y_pred)) 

error = []

# Calculating error for K values between 1 and 40
for i in range(1, 40):  
    knn = KNeighborsClassifier(n_neighbors=i)
    knn.fit(X_train, y_train)
    pred_i = knn.predict(X_test)
    error.append(np.mean(pred_i != y_test))
    
plt.figure(figsize=(12, 6))  
plt.plot(range(1, 40), error, color='red', linestyle='dashed', marker='o',  
         markerfacecolor='blue', markersize=10)
plt.title('Error Rate K Value')  
plt.xlabel('K Value')  
plt.ylabel('Mean Error') 

plt.show()